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EP4532762A1 - Séquençage monomoléculaire et établissement du profil de méthylation de l'adn acellulaire - Google Patents

Séquençage monomoléculaire et établissement du profil de méthylation de l'adn acellulaire

Info

Publication number
EP4532762A1
EP4532762A1 EP23816680.5A EP23816680A EP4532762A1 EP 4532762 A1 EP4532762 A1 EP 4532762A1 EP 23816680 A EP23816680 A EP 23816680A EP 4532762 A1 EP4532762 A1 EP 4532762A1
Authority
EP
European Patent Office
Prior art keywords
cfdna
sequencing
methylation
cancer
dna
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
EP23816680.5A
Other languages
German (de)
English (en)
Inventor
Billy Tsz Cheong Lau
Hanlee P. Ji
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Leland Stanford Junior University
Original Assignee
Leland Stanford Junior University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Leland Stanford Junior University filed Critical Leland Stanford Junior University
Publication of EP4532762A1 publication Critical patent/EP4532762A1/fr
Pending legal-status Critical Current

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Classifications

    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
    • G16H10/40—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for data related to laboratory analysis, e.g. patient specimen analysis
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
    • C12Q1/6886—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12N—MICROORGANISMS OR ENZYMES; COMPOSITIONS THEREOF; PROPAGATING, PRESERVING, OR MAINTAINING MICROORGANISMS; MUTATION OR GENETIC ENGINEERING; CULTURE MEDIA
    • C12N15/00—Mutation or genetic engineering; DNA or RNA concerning genetic engineering, vectors, e.g. plasmids, or their isolation, preparation or purification; Use of hosts therefor
    • C12N15/09—Recombinant DNA-technology
    • C12N15/10—Processes for the isolation, preparation or purification of DNA or RNA
    • C12N15/1034—Isolating an individual clone by screening libraries
    • C12N15/1065—Preparation or screening of tagged libraries, e.g. tagged microorganisms by STM-mutagenesis, tagged polynucleotides, gene tags
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B30/00—ICT specially adapted for sequence analysis involving nucleotides or amino acids
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G16H20/10—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/154—Methylation markers
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B30/00—ICT specially adapted for sequence analysis involving nucleotides or amino acids
    • G16B30/10—Sequence alignment; Homology search
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
    • G16B40/20—Supervised data analysis

Definitions

  • cfDNA cell-free DNA
  • ctDNA circulating tumor DNA
  • the present method allows on to characterize methylation patterns from cell- free DNA isolated from body fluids, particularly from cancer patients, without PCR (FIG. 1 A). This approach is believed to overcome some of the potential problems with conventional methylation sequencing of cfDNA.
  • the methods disclosed herein comprise characterizing methylated DNA without any chemical or enzymatic conversion, as required with short-read approaches.
  • the present methods do not utilize PCR amplification, thus enabling single-molecule counting of cfDNA molecules without UMI (unique molecular index) barcodes.
  • Methylated DNA generates a unique single molecule sequencing signal compared to unmodified DNA, and is readily detected with various machine learning algorithms. Therefore, single molecule sequencing methylation profiles directly reflect the native state of the cfDNA without the typical skews and biases introduced through conventional methods of DNA sequencing preparation.
  • FIGS. 2A-2D Single-molecule methylated sequence classification.
  • A Overview of method. Reads are classified alongside a set of candidate sample reference methylomes to determine a potential matching sample type. Sites are merged between the aligned read and candidate methylome, after which methylation states are compared.
  • B Classification accuracy. GP2D and healthy donor-derived nucleosome mixtures were used to validate the classification procedure. ROC curves are plotted, where each curve represents a distinct immune threshold score. The curve is plotted by varying the cancer threshold score.
  • C Admixture validation. The proportion of reads classified as belonging to cell line reference is plotted as a function of the actual admixture ratio and sequencing depth.
  • FIG. 5 Gene list enrichment analysis showing significant hits in the Myc pathway.
  • FIG. 9 Experimental and bioinformatics steps of certain exemplary methods disclosed herein.
  • a “subject” or “patient” as used herein can be a human or a non-human animal.
  • a non-human animal can be a primate, a canine, a feline, a bovine, or an equine animal.
  • Methylation profiling of cfDNA has previously been shown to identify correlative features such as tissue-of-origin, gene expression, and tumor subtyping - single molecule sequencing, by the virtue of native DNA processing, will help accelerate this process.
  • the methods disclosed herein can significantly expand on epigenomic analysis of cell-free DNA, which can significantly impact liquid biopsy -based diagnosis for cancer as well as monitoring of disease progression or efficacy of a cancer therapy administered to a subject.
  • Certain embodiments of the disclosure provide a method for detecting a molecule of circulating tumor DNA (ctDNA) in a sample of cell-free DNA (cfDNA).
  • the method comprises sequencing the sample of cfDNA using a single molecule sequencing to obtain sequence reads.
  • a “differentially methylated CpG site” as used herein refers to a CpG site that differs in its methylation status between a cancer cell versus a non-cancer cell.
  • a differentially methylated CpG site can be identified based on the genomic co-ordinates of the CpG site.
  • a differentially methylated CpG site in a human can be identified based on its co-ordinates in the human genome, for example, in the GRCh38 reference human genome.
  • a CpG site can also be partially methylated, with methylation values in between 100% (methylated) and 0% (non-methylated) methylation.
  • a differentially methylated site is also identified as a partially methylated site where the methylation value differs between a cancer cell versus a non-cancer cell.
  • the methylation status of the differentially methylated CpG sites in a sequence read is used to determine a methylation profile for the sequence read.
  • a methylation profile of a sequence read provides methylation status of differentially methylated CpG sites in the sequence read.
  • the first methylation score indicates the extent of similarity between methylation status of differentially methylated CpG sites in a sequence read with the methylation status of the differentially methylated CpG sites in a cancer cell.
  • the first methylation score is also referenced in this disclosure as “tumor score.”
  • An example of first methylation scores (tumor scores) for sequence reads from cancer cells is provided in FIG. 6.
  • the first and the second methylation scores can be used to identify a sequence read as being from a molecule of tDNA.
  • Various calculations and/or comparisons can be used to identify a sequence read as being or not being from a molecule of tDNA based on the first and the second methylation scores.
  • a threshold is 0.5
  • a sequence read is identified as not being from a molecule of tDNA if the second methylation score is 0.5 or above, i.e., at least half of the differentially methylated CpG sites in a sequence read have the same methylation status as that of a non-cancer cell.
  • a threshold is 0.8
  • a sequence read is identified as not being from a molecule of tDNA if the second methylation score is 0.8 or above, i.e., at least 80% of the differentially methylated CpG sites in a sequence read have the same methylation status as that of a noncancer cell.
  • the two thresholds are numerically different from each other, for example: the first threshold is 0.7, 0.8, or 0.9 and the second threshold is 0.7, 0.8, or 0.9 but is different from the first threshold.
  • the mono-nucleosome to di- nucleosome ratio may be calculated. This ratio can also be from numbers of at least 1 , such as 1 , 2, 3, 4, 5, 6, 7, 8, 9, or at 10.
  • the single molecule sequencing is nanopore-based sequencing.
  • the single molecule sequencing is single molecule real time (SMRT) sequencing.
  • identifying a plurality of sequence reads from a cfDNA sample as being or not being from a molecule of tDNA can be used to estimate the number of molecules of tDNA in a sample of cfDNA.
  • the proportion of tDNA molecules in a cfDNA sample can be used to estimate “tumor load” of the cfDNA sample.
  • a tumor load is calculated as the percentage of sequence reads identified as being from a molecule of tDNA as compared to the number of sequence reads in a cfDNA sample for which an identification is made. Thus, in this calculation, sequence reads that cannot be definitively identified as being or not being from a molecule of tDNA are ignored. For example, if a million sequence reads are produced from a cfDNA sample and 1 ,000 reads are identified as being from tDNA and 500,000 reads cannot be definitively identified as being or not being from a molecule of tDNA, then the tumor load of that cfDNA sample is 0.2%.
  • a tumor load of cfDNA sample from a subject can be used to estimate the disease status in a cancer patient. Such status can be used to diagnose cancer in a subject, monitor cancer progression in a subject, or monitor efficacy of a cancer therapy administered to a subject. Accordingly, certain embodiments of the disclosure provide a method of diagnosing cancer in a subject by estimating a tumor load in the subject according to the methods disclosed herein and identifying the presence of cancer in the subject if the tumor load is at or above a threshold.
  • the cancer therapy is effective in treating cancer in the subject. Also, the magnitude of decrease would indicate the efficacy of the cancer therapy. A bigger decrease in the tumor load would indicate more efficacious cancer therapy, whereas a relatively smaller decrease in the tumor load would indicate a relatively less efficacious cancer therapy.
  • producing end-repaired and A-tailed cfDNA comprises incubating the cfDNA with an end-repair and A-tailing enzyme mix for at least 30 minutes.
  • the optimized library preparation disclosed herein allows using lower amounts of initial cfDNA used to prepare the cfDNA library.
  • the amount of cfDNA used in producing the A-tailed cfDNA is between 100 pg and 5 ng, between 800 ng and 1.5 ng, or about 1 ng.
  • the methods described in this disclosure find use in a variety of applications. Applications of interest include, but are not limited to: research applications and therapeutic applications. Methods of the disclosure find use in a variety of different applications including any convenient application where identifying methylation profiles of cfDNA is desired.
  • the method finds particular use in detecting the presence of tDNA in cfDNA samples obtained from a subject.
  • Tumor load calculated according to the methods disclosed herein can be used to monitor the progression of a cancer in a subject. For example, increasing tumor load can indicate advancing disease, whereas decreasing tumor load can indicate cancer remission.
  • the EZ Nucleosomal DNA Prep Kit (Zymo Research) was used. This method uses DNAse to digest open chromatin positions and yields a fragment pattern characteristic of cell- free DNA instead of random fragmentation. Briefly, nuclei were processed from whole cells by the addition of a nuclei prep buffer that lyses the cell membrane but leaves the nuclei membrane intact. Enzymatic DNAse digestion then fragments DNA at unprotected locations, after which DNA is purified with the kit’s included components. For nucleosomes from cancer cell lines, adherent cells treated with trypsin were used.
  • Mag-Bind Total NGS beads (Omega Bio-Tek; an alternative to Ampure XP beads) were added and mixed to each reaction. After incubation for 5 minutes, the mixtures were pooled together into a 50 pl centrifuge tube. The beads were magnetized and washed with 80% ethanol using a DynaMag separation rack (Thermo Fisher Scientific) before eluting in 600 pl of 10 mM Tris-HCI pH 8.0 buffer. A second bead cleanup step was performed with 900 pl Mag-Bind Total NGS beads (1 .5X ratio) and the same magnetic rack procedure. The elution solution was 50 pl 10 mM Tris-HCI pH 8.0 buffer.

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Abstract

La présente invention concerne des procédés de détection d'une molécule d'ADN tumoral (ADNt) dans un échantillon d'ADN acellulaire (ADNa). Dans certains modes de réalisation, l'ADNa est séquencé à l'aide d'un séquençage monomoléculaire afin d'obtenir un profil de méthylation d'une lecture de séquence. Ce profil de méthylation est comparé à un profil de méthylation de référence provenant d'une cellule cancéreuse et/ou d'une cellule non cancéreuse afin d'identifier la séquence lue comme provenant d'une molécule d'ADNt. D'autres modes de réalisation permettent d'estimer le nombre de molécules d'ADNa dans l'échantillon d'ADNa et, pour le considérer comme une charge tumorale de l'ADNa, de déterminer la proportion du nombre de molécules d'ADNa par rapport au nombre total de molécules d'ADNa dans l'échantillon. Une telle charge tumorale peut être utilisée pour suivre la progression du cancer chez un sujet ou l'efficacité d'une thérapie anticancéreuse administrée à un sujet.
EP23816680.5A 2022-06-02 2023-05-31 Séquençage monomoléculaire et établissement du profil de méthylation de l'adn acellulaire Pending EP4532762A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US202263348425P 2022-06-02 2022-06-02
PCT/US2023/023970 WO2023235379A1 (fr) 2022-06-02 2023-05-31 Séquençage monomoléculaire et établissement du profil de méthylation de l'adn acellulaire

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EP4532762A1 true EP4532762A1 (fr) 2025-04-09

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EP23816680.5A Pending EP4532762A1 (fr) 2022-06-02 2023-05-31 Séquençage monomoléculaire et établissement du profil de méthylation de l'adn acellulaire

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US (1) US20250313898A1 (fr)
EP (1) EP4532762A1 (fr)
WO (1) WO2023235379A1 (fr)

Families Citing this family (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2024129712A1 (fr) * 2022-12-12 2024-06-20 Flagship Pioneering Innovations, Vi, Llc Informations de séquençage en phase à partir d'adn tumoral en circulation
WO2025247632A1 (fr) 2024-05-27 2025-12-04 European Molecular Biology Laboratory Préparation d'acides nucléiques fragmentés acellulaires pour le séquençage d'analyse génétique

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP4322168A3 (fr) * 2016-07-06 2024-05-15 Guardant Health, Inc. Procédés de profilage de fragmentome d'acides nucléiques acellulaires
CA3063405A1 (fr) * 2017-05-12 2018-11-15 President And Fellows Of Harvard College Diagnostic de cancer precoce universel

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WO2023235379A1 (fr) 2023-12-07
US20250313898A1 (en) 2025-10-09

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